The project objective is to develop statistical methods to estimate duration (survival) models, and to apply these methods to nursing home and mental health duration data. In spite of increasing interest in nursing home utilization among the elderly, there have not been corresponding improvements in our knowledge of how long people stay in nursing homes. In particular, elderly people who enter nursing homes as private patients face the risk of spending-down their assets until they become eligible for Medicaid, and it is not known how great this risk is. The first aim is to develop and validate on several data sets hierarchical models incorporating linear multiple regression terms that will provide estimates of important policy variables, while accounting for commonly encountered sampling problems. These sampling problems include censoring and length-biased sampling. The hierarchical modeling framework is important because it enables fitting individual-level models, permits heterogeneity across individuals, and results in more general (mixture) duration distributions. The other two aims are to apply the models developed to improve estimates of nursing home length of stay and the probability of spend-down. The nursing home data set includes all residents in thirty-six San Diego nursing homes during a year and a half period during the early 1980s. As second major focus will be an estimating length of stay of inpatients in mental health facilities, using data from Massachusetts during 1985-1992. The methods developed will be applicable to other health data sets.